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Vanishing gradients (values get multiplied by small values in each RNN step) and exploding gradients are common RNN problems that occur when training during the backpropagation step. LSTM provides a dedicated cell state that is modified by forget, input, and output gates to alleviate those problems.
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Gated recurrent units (GRUs) are simpler but less expressive memory cells, where the forget and input gates are combined into a single update gate.
- For a 3/4 time signature, you need 3 steps per quarter note, times 4 steps per quarter note, which equals 12 steps per bar. For a binary step counter to count to 12, you need 5 bits (like for 4/4 time) that will only count to 12. For 3 lookbacks, you'll need to look at the past 3 bars, with each bar being 12 steps, so you have [36, 24, 12].
- The resulting vector is the sum of the previous...
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